Computing with viruses

In recent years, different computing models have emerged within the area of Unconven-tional Computation, and more specifically within Natural Computing, getting inspiration from mechanisms present in Nature. In this work, we incorporate concepts in virology and theoretical computer science to propose a novel computational model, called Virus Ma-chine. Inspired by the manner in which viruses transmit from one host to another, a virus machine is a computational paradigm represented as a heterogeneous network that con-sists of three subnetworks: virus transmission, instruction transfer, and instruction-channel control networks. Virus machines provide non-deterministic sequential devices. As num-ber computing devices, virus machines are proved to be computationally complete, that is, equivalent in power to Turing machines. Nevertheless, when some limitations are imposed with respect to the number of viruses present in the system, then a characterization for semi-linear sets is obtained

Hepatitis E virus infection in swine workers: A meta‐analysis

Hepatitis E virus (HEV) infects both humans and animals. Swine has been confirmed to be the principal natural reservoir, which raises a concern that HEV infection would be substantially increasing among swine workers. The present study calculated the pooled prevalence of IgG antibodies against HEV among swine workers and the general population in previous cross‐sectional studies. We conducted a meta‐analysis comparing the prevalence of HEV infection between swine workers and the general population, including local residents, blood donors and non‐swine workers. Through searches in three databases (PubMed and OVID in English, and CNKI in Chinese) and after study selection, a total of 32 studies from 16 countries (from 1999 through 2018) were included in the meta‐analysis. A random‐effect model was employed in the study; an I 2 statistic assessed heterogeneity, and the Egger’s test detected publication bias. The comparative prevalence of anti‐HEV IgG was pooled from the studies. Compared to the general population, the prevalence ratio (PR) for swine workers was estimated to be 1.52 (95% CI 1.38–1.76) with the I 2 being 71%. No publication bias was detected (p = 0.40). A subgroup analysis further indicated increased prevalence of anti‐HEV IgG in the swine workers in Asia (PR = 1.49, 95% CI: 1.35–1.64), in Europe (PR = 1.93, 95% CI: 1.49–2.50) and in all five swine‐related occupations, including swine farmers, butchers, meat processors, pork retailers and veterinarians (PR ranged between 1.19 and 1.75). In summary, swine workers have a relatively higher prevalence of past HEV infection, and this finding is true across swine‐related occupations, which confirms zoonotic transmission between swine and swine workers.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147857/1/zph12548_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147857/2/zph12548.pd

Embedded Based Miniaturized Universal Electrochemical Sensing Platform

We created an embedded sensing platform based on STM32 embedded system, with integrated carbon-electrode ionic sensor by using a self-made plug. Given ration of concentration-unknown nitrate liquid samples, this platform is able to measure the nitrate concentration in neutral environment. Response signals which were transmitted by the sensor can be displayed via a serial port to the computer screen or via Bluetooth to the smartphone. Processed by a fitting function, signals are transformed into related concentration. Through repeating the experiment many times, the accuracy and repeatability turned out to be excellent. The results can be automatically stored on smartphone via Bluetooth. We created this embedded sensing platform for field water quality measurement. This platform also can be applied for other micro sensors’ signal acquisition and data processing

LRBmat: A Novel Gut Microbial Interaction and Individual Heterogeneity Inference Method for Colorectal Cancer

Many diseases are considered to be closely related to the changes in the gut microbial community, including colorectal cancer (CRC), which is one of the most common cancers in the world. The diagnostic classification and etiological analysis of CRC are two critical issues worthy of attention. Many methods adopt gut microbiota to solve it, but few of them simultaneously take into account the complex interactions and individual heterogeneity of gut microbiota, which are two common and important issues in genetics and intestinal microbiology, especially in high-dimensional cases. In this paper, a novel method with a Binary matrix based on Logistic Regression (LRBmat) is proposed to deal with the above problem. The binary matrix can directly weakened or avoided the influence of heterogeneity, and also contain the information about gut microbial interactions with any order. Moreover, LRBmat has a powerful generalization, it can combine with any machine learning method and enhance them. The real data analysis on CRC validates the proposed method, which has the best classification performance compared with the state-of-the-art. Furthermore, the association rules extracted from the binary matrix of the real data align well with the biological properties and existing literatures, which are helpful for the etiological analysis of CRC. The source codes for LRBmat are available at https://github.com/tsnm1/LRBmat

Weather system classification of local hourly heavy rainfall in Jiangxi Province

Based on the conventional high-altitude and ground observation datasets and the 1-hour rainfall datasets from 93 national auto? matic weather stations in Jiangxi from April to September of 1998 to 2019, a total of 204 selected local hourly heavy rainfall events (hereinaf? ter LHR) in Jiangxi were analyzed and classified. With the analysis of synoptic meteorology, radiosonde, and physical properties, the concep? tual models of each category of these LHR events were established. The results are as follows. LHR in Jiangxi can be classified into 5 catego? ries, including Pre-Trough pattern (PRT), Post-Trough pattern (POT), Tropical System pattern (TS), Edge of Subtropical High pattern (ESH), and Control of Subtropical High (CSH) pattern. PRT is the most common type, which accounts for 48% of the total events. These events usual? ly occur in front of the high-altitude trough, near the mid-low-level shear lines, and are usually associated with cold fronts, stationary fronts, or low-pressure troughs on the surface. The second most frequent one is TS, which accounts for 19.1% of the total events. TS can also be divid? ed into the tropical cyclone type and the east wind wave type. The structure and movement of the tropical systems can significantly affect the area of LHR. ESH is divided into western ESH, southern ESH, and northern ESH. For this type, LHR events usually occur near the edge of the subtropical high 588 dagpm line, the low layer shear line, or the convergence line. CSH accounts for 5.5% of the total events. LHR happens when the subtropical high controls the entire Jiangxi Province. Particularly, when a center temperature at 500 hPa over the northern or eastern Jiangnan regions below -4℃ appears under the background of a cold trough, LHR events frequently occur on the ground convergence line, in the high-temperature area, or windward slope of mountains. POT is the least common type, which accounts for 4.4% of the events. It occurs under the northwesterly flow behind a trough, in the exit zone of the low-level jets, in the convergence zone, or on the surface convergence line

Hyperbolic Graph Diffusion Model

Diffusion generative models (DMs) have achieved promising results in image and graph generation. However, real-world graphs, such as social networks, molecular graphs, and traffic graphs, generally share non-Euclidean topologies and hidden hierarchies. For example, the degree distributions of graphs are mostly power-law distributions. The current latent diffusion model embeds the hierarchical data in a Euclidean space, which leads to distortions and interferes with modeling the distribution. Instead, hyperbolic space has been found to be more suitable for capturing complex hierarchical structures due to its exponential growth property. In order to simultaneously utilize the data generation capabilities of diffusion models and the ability of hyperbolic embeddings to extract latent hierarchical distributions, we propose a novel graph generation method called, Hyperbolic Graph Diffusion Model (HGDM), which consists of an auto-encoder to encode nodes into successive hyperbolic embeddings, and a DM that operates in the hyperbolic latent space. HGDM captures the crucial graph structure distributions by constructing a hyperbolic potential node space that incorporates edge information. Extensive experiments show that HGDM achieves better performance in generic graph and molecule generation benchmarks, with a $48\%$ improvement in the quality of graph generation with highly hierarchical structures.Comment: accepted by AAAI 202